Event Details

Improvements in neural recording technologies have rapidly increased the number of neurons that it is now possible to record from. Along with these improvements, analyses of neural information processing are moving from single neuron to population-level analyses. One promising approach for understanding information processing across large populations of neurons is to use methods for dimensionality reduction; such approaches aim to find low-dimensional structure in the joint activity of many neurons over time. In this talk, I will describe my lab's efforts to learn low-dimensional structure present in large-scale neural recordings, both from electrophysiology recordings in motor cortex and from two-photon calcium movies in primary visual cortex. Our findings suggest that dimensionality reduction techniques can be used to pull out structure from neural activity to solve a range of decoding and classification problems.